Evidence map›Paper›PMID 39228725›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Adoption of the OMOP CDM for Cancer Research using Real-world Data: Current Status and Opportunities.

Liwei Wang, Andrew Wen, Sunyang Fu, Xiaoyang Ruan, Ming Huang, Rui Li, Qiuhao Lu, Andrew E Williams, Hongfang Liu

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Liwei WangMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, TX, USA.
Andrew WenMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, TX, USA.
Sunyang FuMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, TX, USA.
Xiaoyang RuanMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, TX, USA.
Ming HuangMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, TX, USA.
Rui LiMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, TX, USA.
Qiuhao LuMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, TX, USA.
Andrew E WilliamsClinical and Translational Science Institute Tufts Medical Center Boston US.
Hongfang LiuMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, TX, USA.

Funding

Semi-structured Information Retrieval in Clinical Text for Cohort IdentificationR01LM011934 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI HERSH, WILLIAM R, LIU, HONGFANG · 2014 to 2025
$5.1M
Predictive modeling of Alzheimer's Disease Related Dementias (ADRD) in the elderly population empowered by knowledge-driven data miningR01HG012748 · NHGRI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI HONGFANG LIU · 2023 to 2026
$3.1M
Facilitate Observational Studies of Alzheimer's Disease and Alzheimer's Disease-Related Dementias Using Ontology and Natural Language ProcessingRF1AG072799 · NIA · YALE UNIVERSITY · PI LIU, HONGFANG, TAO, CUI · 2021 to 2021
$2.4M
NHGRI NIH HHS R01 HG012748NIA NIH HHS RF1 AG072799NLM NIH HHS R01 LM011934
6 · The paper itself

Abstract

Background: The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) that is developed and maintained by the Observational Health Data Sciences and Informatics (OHDSI) community supports large scale cancer research by enabling distributed network analysis. As the number of studies using the OMOP CDM for cancer research increases, there is a growing need for an overview of the scope of cancer research that relies on the OMOP CDM ecosystem. Objectives: In this study, we present a comprehensive review of the adoption of the OMOP CDM for cancer research and offer some insights on opportunities in leveraging the OMOP CDM ecosystem for advancing cancer research. Materials and Methods: Published literature databases were searched to retrieve OMOP CDM and cancer-related English language articles published between January 2010 and December 2023. A charting form was developed for two main themes, i.e., clinically focused data analysis studies and infrastructure development studies in the cancer domain. Results: In total, 50 unique articles were included, with 30 for the data analysis theme and 23 for the infrastructure theme, with 3 articles belonging to both themes. The topics covered by the existing body of research was depicted. Conclusion: Through depicting the status quo of research efforts to improve or leverage the potential of the OMOP CDM ecosystem for advancing cancer research, we identify challenges and opportunities surrounding data analysis and infrastructure including data quality, advanced analytics methodology adoption, in-depth phenotypic data inclusion through NLP, and multisite evaluation.

Indexed as

cancer researchCommon Data Model (CDM)Observational Health Data Sciences and Informatics (OHDSI) networkObservational Medical Outcomes Partnership (OMOP)Real-world datascoping review

Identifiers

PMID39228725
PMCPMC11370549

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.